Adaptive potential Merge genetic data
Bioshifts group
1 Load libraries and source code
# devtools::install_github('EcologicalTraitData/traitdataform')
rm(list=ls())
list.of.packages <- c("ggplot2","data.table","dplyr","tidyr","parallel","bdc","taxadb","traitdataform","pbapply","tidyverse","readxl","lme4","coefplot","sjPlot","sjmisc","effects","rgdal","maptools","rgeos","terra","MuMIn","rnaturalearthdata","lsmeans","GGally","tidyterra","httr","purrr","rlist","usethis")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
sapply(list.of.packages, require, character.only = TRUE)## Warning: package 'terra' was built under R version 4.3.2## ggplot2 data.table dplyr tidyr
## TRUE TRUE TRUE TRUE
## parallel bdc taxadb traitdataform
## TRUE TRUE TRUE TRUE
## pbapply tidyverse readxl lme4
## TRUE TRUE TRUE TRUE
## coefplot sjPlot sjmisc effects
## TRUE TRUE TRUE TRUE
## rgdal maptools rgeos terra
## TRUE TRUE TRUE TRUE
## MuMIn rnaturalearthdata lsmeans GGally
## TRUE TRUE TRUE TRUE
## tidyterra httr purrr rlist
## TRUE TRUE TRUE TRUE
## usethis
## TRUE2 source functions
# Code to find accepted species names
# Before you can download the source code from github, make sure you have a personal github token
# run this:
# usethis::edit_r_environ()
# if you have no toke, create one following:
# 1. Generate on GitHub your personal token
# 1.1 Go to GitHub
# 2.1 In the right corner go to "Settings"
# 2.2 Then in the left part go to "Developer setting"
# 2.3 Select the option "Personal access tokens"
# 2.4 Select the option "Generate new token"
# 2.5 Copy your personal token
# run this to add the token to your .Renviron
# usethis::edit_r_environ()
# write GITHUB_PAT=YOUR_TOKEN
# Sys.getenv("GITHUB_PAT")
source(here::here("R/fetchGHdata.R"))
fetchGHdata(gh_account = "Bioshifts",
repo = "bioshifts_v1_v2",
path = "R/Source_code/Clean_names.R",
output = here::here("R/Clean_names.R"))## Loading required package: jsonlite##
## Attaching package: 'jsonlite'## The following object is masked from 'package:purrr':
##
## flatten## [1] 0fetchGHdata(gh_account = "Bioshifts",
repo = "bioshifts_v1_v2",
path = "R/Source_code/Find_Sci_Names.R",
output = here::here("R/Find_Sci_Names.R"))## [1] 0source(here::here("R/Clean_names.R"))
source(here::here("R/Find_Sci_Names.R"))
# Malin's transformation for moving values slightly inward.
transform01 <- function(x) (x * (length(x) - 1) + 0.5) / (length(x))
# De Kort transformation
deKort_trans <- function(p){
p <- scale(p)
p <- (p - min(p, na.rm = T))/(max(p, na.rm = T)-min(p, na.rm = T))
return(p)
}
# Malin's transformation before a logit transformation
logit_trans <- function(p){
p <- transform01(p)
p <- log(p/(1-p))
return(p)
}3 Load species list
splist <- read.csv(here::here("Data/splist.csv"), header = T)
# remove duplicated sp_names
splist <- splist %>%
dplyr::filter(!duplicated(scientificName))%>%
mutate(Kingdom=kingdom,
Phylum=phylum,
Class=class,
Order=order,
Family=family)%>%
dplyr::select(scientificName,Kingdom,Phylum,Class,Order,Family,db)
splist$Genus <- sapply(splist$scientificName, function(x){
strsplit(x," ")[[1]][1]
})4 Load bioshifts
4.1 Load v1
biov1 <- read.csv(here::here("Data/biov1_fixednames.csv"), header = T)
# Fix references in biov1
biov1$sp_name_std_v1 <- gsub("_"," ",biov1$sp_name_std_v1)
biov1 <- biov1 %>%
dplyr::select(ID,Article_ID,Study_ID,
Type,Param,Trend,SHIFT,UNIT,DUR,
v.lat.mean,v.ele.mean,
START,END,Sampling,Uncertainty_Distribution,Uncertainty_Parameter,
N,Grain_size,Data,ID.area,
Phylum,Class,Order,Family,Genus,sp_name_std_v1,
group,ECO,Hemisphere) %>% # select columns
mutate(
Type = case_when(
Type=="HOR" ~ "LAT",
TRUE ~ as.character(Type)),
Data = case_when(
Data=="occurence-based" ~ "occurrence-based",
TRUE ~ as.character(Data)),
spp = sp_name_std_v1,
SHIFT_abs = abs(SHIFT),
velocity = ifelse(Type == "LAT", v.lat.mean, v.ele.mean),
vel_sign = ifelse(velocity>0,"pos","neg"))
biov1 <- biov1 %>%
dplyr::filter(!is.na(sp_name_std_v1))
all(biov1$sp_name_std_v1 %in% splist$scientificName)## [1] TRUE# from continuous to categorical
q1=quantile(biov1$START,probs=c(0,0.25,0.5,0.75,1))
biov1$StartF=cut(biov1$START,breaks=q1,include.lowest=T)
q1=quantile(biov1$ID.area,probs=c(0,0.25,0.5,0.75,1))
biov1$AreaF=cut(biov1$ID.area,breaks=q1,include.lowest=T)
q1=quantile(biov1$N,probs=c(0,0.25,0.5,0.75,1))
biov1$NtaxaF=cut(biov1$N,breaks=q1,include.lowest=T)
# add ID to obs
biov1$obs_ID <- paste0("S",1:nrow(biov1))
summary(biov1$velocity)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.8328 0.5415 1.9642 2.2635 2.7543 14.5492 1450summary(biov1$velocity[which(biov1$vel_sign=="pos")])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.009396 0.541507 2.055833 2.411912 2.831932 14.5492304.2 Classify species
# Class species as Terrestrial, Marine or Freshwater
Terv1 <- unique(biov1$sp_name_std_v1[which(biov1$ECO == "T")])
Terrestrials = unique(c(Terv1))
Mar1 <- unique(biov1$sp_name_std_v1[which(biov1$ECO == "M")])
Marine = unique(c(Mar1))
# Freshwater fish
FFishv1 <- unique(biov1$sp_name_std_v1[(biov1$Class == "Actinopterygii" | biov1$Class == "Cephalaspidomorphi") & biov1$ECO == "T"])
FFish = unique(c(FFishv1))
# Marine fish
MFishv1 <- unique(biov1$sp_name_std_v1[biov1$Class == "Actinopterygii" & biov1$ECO == "M"])
MFish = unique(c(MFishv1))
splist$ECO = NA
splist$ECO[which(splist$scientificName %in% Terrestrials)] <- "T"
splist$ECO[which(splist$scientificName %in% Marine)] <- "M"
splist$ECO[which(splist$scientificName %in% MFish)] <- "M"
biov1$ECO = NA
biov1$ECO[which(biov1$sp_name_std_v1 %in% Terrestrials)] <- "T"
biov1$ECO[which(biov1$sp_name_std_v1 %in% Marine)] <- "M"
biov1$ECO[which(biov1$sp_name_std_v1 %in% MFish)] <- "M"
splist$Group = NA
splist$Group[which(splist$Class == "Phaeophyceae")] <- "Chromista"
splist$Kingdom[which(splist$Class == "Phaeophyceae")] <- "Chromista"
splist$Group[which(splist$Phylum == "Rhodophyta")] <- "Seaweed"
splist$Kingdom[which(splist$Phylum == "Rhodophyta")] <- "Plantae"
splist$Group[which(splist$Family == "Elminiidae")] <- "Barnacles"
splist$Group[which(splist$Kingdom == "Bacteria")] <- "Bacteria"
splist$Group[which(splist$Class == "Holothuroidea")] <- "Sea cucumber"
splist$Group[which(splist$Class == "Aves")] <- "Bird"
splist$Group[which(splist$Class == "Insecta")] <- "Insect"
splist$Group[which(splist$Class == "Mammalia")] <- "Mammal"
splist$Group[which(splist$Class == "Arachnida")] <- "Spider"
splist$Kingdom[which(splist$Kingdom == "Viridiplantae")] <- "Plantae"
splist$Kingdom[which(splist$Phylum == "Tracheophyta")] <- "Plantae"
splist$Group[which(splist$Kingdom == "Plantae")] <- "Plant"
splist$Group[which(splist$Class == "Hydrozoa")] <- "Hydrozoa"
splist$Group[which(splist$Class == "Anthozoa")] <- "Sea anemones and corals"
splist$Group[which(splist$Class == "Polychaeta")] <- "Polychaetes"
splist$Group[which(splist$Phylum == "Mollusca")] <- "Molluscs"
splist$Group[which(splist$Class == "Malacostraca")] <- "Crustacean"
splist$Group[which(splist$Class == "Hexanauplia")] <- "Crustacean"
splist$Group[which(splist$Class == "Maxillopoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Ostracoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Branchiopoda")] <- "Crustacean"
splist$Group[which(splist$Class == "Asteroidea")] <- "Starfish"
splist$Group[which(splist$Class == "Ascidiacea")] <- "Ascidians tunicates and sea squirts"
splist$Class[which(splist$Class == "Actinopteri")] <- "Actinopterygii"
splist$Group[which(splist$Class == "Actinopterygii")] <- "Fish"
splist$Group[which(splist$Class == "Elasmobranchii")] <- "Fish"
splist$Group[which(splist$Order == "Perciformes")] <- "Fish"
splist$Group[which(splist$Class == "Chondrichthyes")] <- "Fish"
splist$Group[which(splist$Class == "Holocephali")] <- "Fish"
splist$Group[which(splist$Class == "Cephalaspidomorphi")] <- "Fish"
splist$Group[which(splist$Class == "Echinoidea")] <- "Sea urchin"
splist$Group[which(splist$Class == "Crinoidea")] <- "Crinoid"
splist$Group[which(splist$Class == "Holothuroidea")] <- "Sea cucumber"
splist$Group[which(splist$Class == "Reptilia")] <- "Reptile"
splist$Group[which(splist$Order == "Squamata")] <- "Reptile"
splist$Group[which(splist$Class == "Ophiuroidea")] <- "Brittle stars"
splist$Group[which(splist$Class == "Chilopoda")] <- "Centipedes"
splist$Group[which(splist$Class == "Diplopoda")] <- "Millipedes"
splist$Group[which(splist$Class == "Amphibia")] <- "Amphibian"
splist$Group[which(splist$Kingdom == "Fungi")] <- "Fungi"
splist$Group[which(splist$Order == "Balanomorpha")] <- "Barnacles"
splist$Group[which(splist$Phylum == "Nematoda")] <- "Nematodes"
splist$Group[which(splist$Class == "Myxini")] <- "Hagfish"
splist$Group[which(splist$Kingdom == "Chromista")] <- "Chromista"
splist$Family[which(splist$Genus == "Dendrocopus")] <- "Picidae"
######################################
biov1 <- merge(biov1[,-which(names(biov1) %in% c("Phylum","Class","Order","Family","Genus","Group","ECO"))],
splist[,c("Kingdom","Phylum","Class","Order","Family","Genus","Group","ECO","scientificName")],
by.x = "sp_name_std_v1", by.y = "scientificName",
all.x = T)4.3 Filter LAT / ELE shifts
Use only latitudinal and elevational shifts
# v1
biov1 <- biov1 %>%
dplyr::filter(Type %in% c("ELE","LAT")) # Use LAT ELE shifts
# splist
sps <- unique(biov1$sp_name)
splist <- splist %>% dplyr::filter(scientificName %in% sps)4.4 Remove species identified to the genus level or cf.
if(any(grep("sp[.]",biov1$sp_reported_name_v1))){
biov1 <- biov1 %>% dplyr::filter(!grepl("sp[.]",sp_reported_name_v1))
}
if(any(grep("sp[.]",biov1$sp_name_std_v1))){
biov1 <- biov1 %>% dplyr::filter(!grepl("sp[.]",sp_name_std_v1))
}
if(any(grep("cf[.]",biov1$sp_reported_name_v1))){
biov1 <- biov1 %>% dplyr::filter(!grepl("cf[.]",sp_reported_name_v1))
}
if(any(grep("cf[.]",biov1$sp_name_std_v1))){
biov1 <- biov1 %>% dplyr::filter(!grepl("cf[.]",sp_name_std_v1))
}
#remove freshwater fishes
biov1 <- biov1[-which((biov1$Class == "Actinopterygii" | biov1$Group == "Fish") & biov1$ECO=="T"),]
#remove marine birds
biov1 <- biov1[-which(biov1$Class == "Aves" & biov1$ECO=="M"),]
if(any(grep("sp[.]",splist$scientificName))){
splist <- splist %>% dplyr::filter(!grepl("sp[.]",scientificName))
}
if(any(grep("sp[.]",splist$scientificName))){
splist <- splist %>% dplyr::filter(!grepl("sp[.]",scientificName))
}
if(any(grep("cf[.]",splist$scientificName))){
splist <- splist %>% dplyr::filter(!grepl("cf[.]",scientificName))
}
if(any(grep("cf[.]",splist$scientificName))){
splist <- splist %>% dplyr::filter(!grepl("cf[.]",scientificName))
}4.5 Reclassify methodological variables
unique(biov1$Data)## [1] "abundance-based" "occurrence-based"biov1$Data[biov1$Data=="occurence-based"] <- "occurrence-based"
biov1$Data <- factor(biov1$Data, levels = unique(biov1$Data))
table(biov1$Data)##
## abundance-based occurrence-based
## 9496 20744unique(biov1$Sampling)## [1] "TWO" "CONTINUOUS" "MULTIPLE(continuous)"
## [4] "IRR"biov1$Sampling = ifelse(biov1$Sampling %in% c("IRR","MULTIPLE(continuous)"),"MULTIPLE", biov1$Sampling)
biov1$Sampling <- ordered(biov1$Sampling,
levels = c("TWO","MULTIPLE","CONTINUOUS"))
table(biov1$Sampling)##
## TWO MULTIPLE CONTINUOUS
## 25819 1251 3170unique(biov1$Grain_size)## [1] "small" "large" "moderate" "very_large" NAbiov1$Grain_size <- ifelse(biov1$Grain_size %in% c("large","very_large"),"large",biov1$Grain_size)
biov1$Grain_size <- ordered(biov1$Grain_size,
levels = c("small","moderate","large"))
table(biov1$Grain_size)##
## small moderate large
## 9841 10971 9427unique(biov1$Uncertainty_Distribution)## [1] "RESAMPLING(same)" "RAW"
## [3] "RESAMPLING" "MODEL"
## [5] "RESAMPLING+MODEL" "MODEL+RESAMPLING(same)"
## [7] "RESAMPLING(same)+DETECTABILITY" "RESAMPLING(same)+MODEL"
## [9] "DETECTABILITY"biov1$Uncertainty_Distribution <- ifelse(biov1$Uncertainty_Distribution %in% c("RESAMPLING","RESAMPLING(same)"),"RESAMPLING",
ifelse(biov1$Uncertainty_Distribution %in% c("MODEL","MODEL+RESAMPLING(same)","RESAMPLING+MODEL"),"MODEL",
ifelse(biov1$Uncertainty_Distribution %in% c("DETECTABILITY","RESAMPLING(same)+DETECTABILITY"),"DETECTABILITY",
biov1$Uncertainty_Distribution)))
biov1$Uncertainty_Distribution <- ifelse(biov1$Uncertainty_Distribution == "RAW","OPPORTUNISTIC","PROCESSED")
table(biov1$Uncertainty_Distribution)##
## OPPORTUNISTIC PROCESSED
## 9532 20708#transform study area
biov1$ID.area <- log(biov1$ID.area)4.6 Get centroids of study areas
This will be used to merge genetic data with bioshifts based on distance of observations
# # The input file geodatabase
# fgdb <- "C:/Users/brunn/nextCloud/bioshifts_v1_v2/v1/Study_Areas_v1/Study_Areas.gdb"
#
# # List all feature classes in a file geodatabase
# fc_list <- ogrListLayers(fgdb)
#
# # Get centroids
# cl <- makeCluster(detectCores()-1)
# clusterExport(cl, c("readOGR", "gCentroid", "fgdb"))
#
# centroids <- pblapply(fc_list, function(x){
# fc <- readOGR(dsn=fgdb,layer=x)
# c <- data.frame(gCentroid(fc))
# names(c) <- c("centroid.x", "centroid.y")
#
# geomet <- data.frame(terra::geom(terra::vect(fc)))
#
# max_lat <- order(geomet[,"y"], decreasing = T)[1]
# max_lat <- geomet[max_lat,c("x","y")]
# names(max_lat) <- c("max_lat.x", "max_lat.y")
#
# min_lat <- order(geomet[,"y"])[1]
# min_lat <- data.frame(geomet[min_lat,c("x","y")])
# names(min_lat) <- c("min_lat.x", "min_lat.y")
#
# cbind(c, max_lat, min_lat)
# }, cl = cl)
#
# stopCluster(cl)
#
# centroids <- do.call("rbind",centroids)
# centroids$ID <- fc_list
#
# write.csv(centroids, "Data/centroids_study_areas.csv", row.names = FALSE)
centroids <- read.csv(here::here("Data/centroids_study_areas.csv"))
biov1 <- merge(biov1, centroids, by = "ID")5 Corrected shifts
Just load it in
fetchGHdata(gh_account = "Bioshifts",
repo = "MethodologicalAdjustment",
path = "outputs/biov1_method_corrected_shifts_study_level.csv",
output = here::here("Data/biov1_method_corrected_shifts_study_level.csv"))## [1] 0biov1_corr <- read.csv(here::here("Data/biov1_method_corrected_shifts_study_level.csv"))
# add corrected shifts
biov1 <- merge(biov1,
biov1_corr %>%
select(c("Article_ID","Study_ID","Class","Type","SLDiff1")),
by = c("Article_ID","Study_ID","Class","Type"),
all.x = TRUE)
biov1$SHIFT_cor <- abs(biov1$SHIFT) - biov1$SLDiff1
# change sign for negative shifts
biov1$neg_shifts <- ifelse(sign(biov1$SHIFT) == -1 & biov1$SHIFT != 0, 1, 0)
biov1$SHIFT_cor[which(biov1$neg_shifts == 1)] <- biov1$SHIFT_cor[which(biov1$neg_shifts == 1)] * -16 Direction of shift
Identify direction of shift
biov1 <- biov1 %>%
mutate(shift_sign = ifelse(SHIFT>0,"pos","neg"),
shift_vel_sign = paste0(shift_sign,vel_sign),
shiftC_sign = ifelse(SHIFT_cor>0,"pos","neg"),
shiftC_vel_sign = paste0(shiftC_sign,vel_sign)
) %>%
filter(!is.na(velocity))
ggplot(biov1, aes(x=shift_vel_sign))+
geom_bar()+
coord_flip()+
facet_wrap(Type~Param)
ggplot(biov1, aes(x=shiftC_vel_sign))+
geom_bar()+
coord_flip()+
facet_wrap(Type~Param)
summary(biov1$velocity)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.8328 0.5415 1.9642 2.2637 2.6272 14.5492summary(biov1$velocity[which(biov1$shift_vel_sign=="pospos")])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.009396 0.541507 2.055833 2.464171 2.831932 14.549230summary(biov1$velocity[which(biov1$shift_vel_sign=="negneg")])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.79347 -2.30487 -1.78004 -1.59872 -0.58152 -0.029457 Calculate lags raw
# calculate lags
# positive values are a lag (range shift lower smaller than expected or in opposite directions) and negative values are range shift larger than expected
biov1$lag <- biov1$velocity-biov1$SHIFT
position = which(biov1$vel_sign == "neg")
biov1$lag[position] <- biov1$lag[position] * -1 # Any negative velocity means the sign of lag has to shift.
# Lag 2
# When shift is in opposite sign of velocity, lag = abs(velocity)
biov1$lag2 = biov1$lag
position <- which(biov1$shift_vel_sign == "posneg" | biov1$shift_vel_sign == "negpos")
biov1$lag2[position] <- abs(biov1$velocity[position])
# Lag 3
# When shift is > velocity, lag = 0
biov1$lag3 = biov1$lag2
position <- which((biov1$shift_sign == "pos" & biov1$SHIFT > biov1$velocity) |
(biov1$shift_sign == "neg" & biov1$SHIFT < biov1$velocity))
biov1$lag3[position] <- 0
{
par(mfrow=c(2,2))
plot(lag~velocity, biov1)
plot(lag2~velocity, biov1)
plot(lag3~velocity, biov1)
}
ggplot(biov1, aes(x = Param, y=lag, fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(biov1$lag2, c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")## Warning: Removed 8827 rows containing non-finite values (`stat_boxplot()`).ggplot(biov1, aes(x = Param, y=lag2, fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(biov1$lag2, c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")## Warning: Removed 6044 rows containing non-finite values (`stat_boxplot()`).ggplot(biov1, aes(x = Param, y=log1p(lag3), fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
# scale_y_continuous(limits = quantile(log1p(biov1$lag3), c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")
8 Calculate lags corrected
# calculate lagCs
# positive values are a lagC (range shift lower smaller than expected or in opposite directions) and negative values are range shift larger than expected
biov1$lagC <- biov1$velocity-biov1$SHIFT_cor
position = which(biov1$vel_sign == "neg")
biov1$lagC[position] <- biov1$lagC[position] * -1 # Any negative velocity means the sign of lagC has to shift.
# Lag 2
# When shift is in opposite sign of velocity, lagC == velocity
biov1$lagC2 = biov1$lagC
position <- which(biov1$shiftC_vel_sign == "posneg" | biov1$shiftC_vel_sign == "negpos")
biov1$lagC2[position] <- abs(biov1$velocity[position])
# Lag 3
# When shift is > velocity, lagC = 0
biov1$lagC3 = biov1$lagC2
position <- which((biov1$shiftC_sign == "pos" & biov1$SHIFT_cor > biov1$velocity) |
(biov1$shiftC_sign == "neg" & biov1$SHIFT_cor < biov1$velocity))
biov1$lagC3[position] <- 0
{
par(mfrow=c(2,2))
plot(lagC~velocity, biov1)
plot(lagC2~velocity, biov1)
plot(lagC3~velocity, biov1)
}
ggplot(biov1, aes(x = Param, y=lagC, fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(biov1$lagC2, c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")## Warning: Removed 10765 rows containing non-finite values (`stat_boxplot()`).ggplot(biov1, aes(x = Param, y=lagC2, fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(biov1$lagC2, c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")## Warning: Removed 6573 rows containing non-finite values (`stat_boxplot()`).ggplot(biov1, aes(x = Param, y=log1p(lagC3), fill = Param, color = Param))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
# scale_y_continuous(limits = quantile(log1p(biov1$lagC3), c(0.1, 0.9), na.rm = T))+
facet_wrap(.~Type, scales = "free")## Warning: Removed 655 rows containing non-finite values (`stat_boxplot()`).
9 Corrected vs raw shifts
plot(SHIFT_cor~SHIFT,biov1)
abline(a=0,b=1,col=2)
lm0=lm(SHIFT_cor~SHIFT,biov1)
summary(lm0)##
## Call:
## lm(formula = SHIFT_cor ~ SHIFT, data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4752 -1.0085 0.1219 0.8303 3.4359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.112782 0.007375 15.29 <2e-16 ***
## SHIFT 1.020616 0.001328 768.28 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.241 on 29582 degrees of freedom
## (655 observations deleted due to missingness)
## Multiple R-squared: 0.9523, Adjusted R-squared: 0.9523
## F-statistic: 5.903e+05 on 1 and 29582 DF, p-value: < 2.2e-16hist(biov1$SHIFT,col=rgb(1,0,0,0.5))
hist(biov1$SHIFT_cor,col=rgb(0,0,1,0.5),add=T)
#so by using residuals we decrease the variation in the raw range shift obs, it's like we work on a more homogenous variables as all the variations due to methods variation has been substracted to the shift.
x1=tapply(biov1$SHIFT,biov1$Group,mean)
x2=tapply(biov1$SHIFT_cor,biov1$Group,mean)#lower mean value than true obs
plot(x1~x2, xlab="Mean shift per Group (raw)", ylab="Mean shift per Group (corrected)")
lm0=lm(x1~x2,biov1)
summary(lm0) #it changes many things##
## Call:
## lm(formula = x1 ~ x2, data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73514 -0.03389 0.09699 0.20771 0.47424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.50337 0.14439 -3.486 0.00824 **
## x2 1.01161 0.01014 99.774 1.14e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4498 on 8 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.9992, Adjusted R-squared: 0.9991
## F-statistic: 9955 on 1 and 8 DF, p-value: 1.137e-13x1=tapply(biov1$SHIFT,biov1$Group,var)
x2=tapply(biov1$SHIFT_cor,biov1$Group,var)#lower variance value than in true obs
plot(x1~x2, xlab="Variance shift per Group (raw)", ylab="Variance shift per Group (corrected)")
lm0=lm(x1~x2,biov1)
summary(lm0) #but high positive correlation meaning that relative variation is conserved##
## Call:
## lm(formula = x1 ~ x2, data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.947 -1.086 -0.033 1.921 4.507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.42377 1.92874 -1.257 0.256
## x2 0.98038 0.03793 25.850 2.21e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.435 on 6 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.9911, Adjusted R-squared: 0.9896
## F-statistic: 668.2 on 1 and 6 DF, p-value: 2.21e-07x1=tapply(biov1$SHIFT,biov1$sp_name_std_v1,mean)
x2=tapply(biov1$SHIFT_cor,biov1$sp_name_std_v1,mean)#lower mean value than in true obs
plot(x1~x2, xlab="Mean shift per species (raw)", ylab="Mean shift per species (corrected)")
lm0=lm(x1~x2,biov1)
summary(lm0) #but at the species level, we observe a high positive relationship so the corrected shifts did not change the pattern of range shift=> good##
## Call:
## lm(formula = x1 ~ x2, data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1326 -0.6949 0.0047 0.7381 8.5064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.056877 0.008936 -6.365 2.03e-10 ***
## x2 0.949122 0.001570 604.366 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.938 on 11823 degrees of freedom
## (400 observations deleted due to missingness)
## Multiple R-squared: 0.9686, Adjusted R-squared: 0.9686
## F-statistic: 3.653e+05 on 1 and 11823 DF, p-value: < 2.2e-16#looking at the relationship with climate velocity
#WARNING:here you need to adapt the code: if you look at the elevational range shift so you have to use EleVeloT in order to use the corresponding climate velocity
par(mfrow=c(1,2))
plot(SHIFT~velocity, xlab="SHIFT (raw)", ylab="Velocity",biov1)
lm0=lm(SHIFT~velocity+I(velocity^2),biov1)
summary(lm0)##
## Call:
## lm(formula = SHIFT ~ velocity + I(velocity^2), data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -120.800 -1.647 -0.793 1.235 145.241
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.849723 0.049818 17.057 < 2e-16 ***
## velocity 0.196547 0.029156 6.741 1.6e-11 ***
## I(velocity^2) -0.010877 0.002899 -3.751 0.000176 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.532 on 30236 degrees of freedom
## Multiple R-squared: 0.002488, Adjusted R-squared: 0.002422
## F-statistic: 37.71 on 2 and 30236 DF, p-value: < 2.2e-16x1=seq(min(biov1$velocity,na.rm = T),max(biov1$velocity,na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2)
plot(SHIFT_cor~velocity, xlab="SHIFT (corrected)", ylab="Velocity",biov1)#the pattern is changing a lot
lm0=lm(SHIFT_cor~velocity+I(velocity^2),biov1)
summary(lm0)##
## Call:
## lm(formula = SHIFT_cor ~ velocity + I(velocity^2), data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -119.895 -2.436 -0.109 1.758 144.074
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.936871 0.052087 17.987 < 2e-16 ***
## velocity 0.214470 0.030694 6.987 2.86e-12 ***
## I(velocity^2) -0.011038 0.003114 -3.545 0.000394 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.67 on 29581 degrees of freedom
## (655 observations deleted due to missingness)
## Multiple R-squared: 0.003057, Adjusted R-squared: 0.002989
## F-statistic: 45.35 on 2 and 29581 DF, p-value: < 2.2e-16x1=seq(min(biov1$velocity,na.rm = T),max(biov1$velocity,na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2)
#we observed a relationship for the raw range shift (an unimodal relationship with higher shifts when absolute climate velocity increase).
#for the range shift corrected by method, no relationship with climatic velocity is observed
#looking at the relationship with lag
par(mfrow=c(1,2))
plot(lagC~velocity,biov1,main="corrected lag estimate")
# Lag C2
# When shift is in opposite sign of velocity, lag == velocity
plot(lagC2~velocity,biov1,main="corrected lag estimate with correction for special case")
#change more things than the above lag metrics, still the highly change are observed at extreme negative values
plot(lag2~lagC2,biov1)
lm0=lm(lag~lagC,biov1)
summary(lm0) #we observe a high positive relationship so the corrected lag did not change the pattern of the observed lag=> good##
## Call:
## lm(formula = lag ~ lagC, data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3871 -0.9069 -0.0191 0.7850 7.9208
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.179997 0.007104 25.34 <2e-16 ***
## lagC 0.943464 0.001152 818.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.2 on 29582 degrees of freedom
## (655 observations deleted due to missingness)
## Multiple R-squared: 0.9577, Adjusted R-squared: 0.9577
## F-statistic: 6.705e+05 on 1 and 29582 DF, p-value: < 2.2e-16par(mfrow=c(1,2))
plot(lag2~velocity,biov1)
lm0=lm(lag2~velocity+I(velocity^2),biov1)
summary(lm0) #significant; R2=46%##
## Call:
## lm(formula = lag2 ~ velocity + I(velocity^2), data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -144.654 -0.356 1.118 2.108 6.504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.895599 0.038423 -23.31 <2e-16 ***
## velocity 0.277031 0.022487 12.32 <2e-16 ***
## I(velocity^2) 0.054080 0.002236 24.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.266 on 30236 degrees of freedom
## Multiple R-squared: 0.1873, Adjusted R-squared: 0.1873
## F-statistic: 3484 on 2 and 30236 DF, p-value: < 2.2e-16x1=seq(min(biov1$velocity, na.rm = T),max(biov1$velocity, na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2) #bimodal relationship
plot(lagC2~velocity,biov1)
lm0=lm(lagC2~velocity+I(velocity^2),biov1)
summary(lm0)#significant; R2=86##
## Call:
## lm(formula = lagC2 ~ velocity + I(velocity^2), data = biov1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -143.281 -0.624 1.177 2.150 6.533
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.160582 0.038835 -29.89 <2e-16 ***
## velocity 0.235309 0.022885 10.28 <2e-16 ***
## I(velocity^2) 0.053876 0.002322 23.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.227 on 29581 degrees of freedom
## (655 observations deleted due to missingness)
## Multiple R-squared: 0.1656, Adjusted R-squared: 0.1655
## F-statistic: 2935 on 2 and 29581 DF, p-value: < 2.2e-16x1=seq(min(biov1$velocity, na.rm = T),max(biov1$velocity, na.rm = T),le=100)
p1=predict(lm0,newdata=data.frame(velocity=x1),type="response")
points(p1~x1,type="l",col=2,lwd=2) #bimodal relationship
10 All shifts vs same direction
ggplot(biov1, aes(x = velocity, y = SHIFT))+
geom_point(aes(color = lag2), alpha = .1)+
scale_color_viridis_c()+
geom_smooth(method = "lm")+
theme_classic()+
facet_wrap(Param~Type+ECO, scales = "free")## `geom_smooth()` using formula = 'y ~ x'
ggplot(biov1 %>%
filter(shift_vel_sign == "pospos" | shift_vel_sign == "negneg"),
aes(x = abs(velocity), y = abs(SHIFT)))+
geom_point(aes(color = lag2), alpha = .1)+
scale_color_viridis_c()+
geom_smooth(method = "lm")+
theme_classic()+
facet_wrap(Param~Type+ECO, scales = "free")## `geom_smooth()` using formula = 'y ~ x'
ggplot(biov1, aes(x = velocity, y = SHIFT_cor))+
geom_point(aes(color = lag2), alpha = .1)+
scale_color_viridis_c()+
geom_smooth(method = "lm")+
theme_classic()+
facet_wrap(Param~Type+ECO, scales = "free")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 655 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 655 rows containing missing values (`geom_point()`).
ggplot(biov1 %>%
filter(shift_vel_sign == "pospos" | shift_vel_sign == "negneg"),
aes(x = abs(velocity), y = abs(SHIFT_cor)))+
geom_point(aes(color = lag2), alpha = .1)+
scale_color_viridis_c()+
geom_smooth(method = "lm")+
theme_classic()+
facet_wrap(Param~Type+ECO, scales = "free")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 259 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 259 rows containing missing values (`geom_point()`).
11 Load genetic data
Datasets used here:
Pinsky, unpublished: Microsatellites and mitochondrial DNA Data for marine fishes.
De Kort et al. 2021 Nat Comm: Microsatellites and AFPL Used expected heterozygosity as a metric of genetic diversity (GDP). It is directly related to effective population size. Because GDP is very sensitive to marker type (e.g. GDP is restricted between 0 and 0.5 in AFLP markers and between 0 and 1 in microsatellite markers), GDP values from each marker type were standardized (mean = 0 and variance = 1) to make them comparable across studies. Standardized GDP values were then normalized as (GDP_scaled-min)/(max-min) to range from 0 to 1. Data for: plant, amphibian, reptile, bird, mammal, and mollusc.
Lawrence et al. 2019 Sci Data: MacroPopGen database. Microsatellites Site-level estimates of genetic diversity from microsatellite markers for vertebrate species (terrestrial vertebrates and freshwater fish) across North and South America. Used FST or expected heterozygosity as a metric of genetic diversity (GDP). Data for: amphibians, birds, fish [anadromous, brackish, catadromous, or freshwater], mammals, and reptiles.
Fonseca et al. 2023 Evo Letters: Mitochondrial DNA (for vertebrates, arachnids, and insects. Chloroplast DNA and nucleotide diversity (for plants). (π) was calculated to describe patterns of intraspecific genetic diversity.
Canteri et al. 2021 Ecography: (We wont use this data because it has no coordinates) Mitochondrial DNA (mtDNA) cytochrome b diversity. Measured genetic diversity at the species level with nucleotide diversity. Data for 1036 bird species.
# Pinsky mtdna
mt <- fread(here::here('Data/mtdna.csv'))
mt$spp <- Clean_Names(mt$spp,
return_gen_sps = TRUE)
# Pinsky msat
ms <- fread(here::here('Data/msat.csv'))
ms$spp <- Clean_Names(ms$spp,
return_gen_sps = TRUE)
# De Kort et al. 2021
gen_d <- fread(here::here('Data/Deposited_data_genetic_diversity_dekort2021.csv'))
gen_d$spp <- gsub("_"," ",gen_d$Species)
gen_d$spp <- Clean_Names(gen_d$spp,
return_gen_sps = TRUE)
# Lawrence et al. 2019: MacroPopGen
gen_lf <- read_excel(here::here("Data/MacroPopGen_Database_final-v0.2.xlsx"), sheet = "MacroPopGen_Database")
gen_lf$spp <- gsub("_"," ",gen_lf$G_s)
gen_lf$spp <- Clean_Names(gen_lf$spp,
return_gen_sps = TRUE)
# Fonseca et al. 2023
gen_fon <- fread(here::here("Data/Fonseca_etal_2023_EvoLetters.txt"))
gen_fon$spp <- gen_fon$Species
gen_fon$spp <- gsub("-"," ",gen_fon$spp)
gen_fon$spp <- gsub("_"," ",gen_fon$spp)
gen_fon$spp <- Clean_Names(gen_fon$spp,
return_gen_sps = TRUE)
# Phenotypic rates
pheno <- fread(here::here('Data/PROCEEDv6_RatesDB.csv'))
pheno$spp <- Clean_Names(pheno$sp_ncbi,
return_gen_sps = TRUE)
##############################
# Remove species identified to the genus level or cf. species
all_sps <- unique(c(gen_d$spp,gen_lf$spp,gen_fon$spp,mt$spp,ms$spp,pheno$spp))
length(all_sps)
spgen <- sapply(all_sps, function(x){
tmp. <- strsplit(x, " ")[[1]]
any(tmp. == "sp" | tmp. == "spp" | grepl("sp[.]",tmp.) | grepl("spp[.]",tmp.) | tmp. == "cf[.]" | tmp. == "cf" | length(tmp.) == 1 | length(tmp.) > 2)
})
spgen <- which(spgen)
spgen <- all_sps[-spgen]
all(spgen %in% all_sps)
all_sps <- all_sps[which(all_sps %in% spgen)]
length(all_sps) # N species after filtering species identified to the species level
# filter each dataset
gen_d <- gen_d[which(gen_d$spp %in% all_sps),]
gen_lf <- gen_lf[which(gen_lf$spp %in% all_sps),]
gen_fon <- gen_fon[which(gen_fon$spp %in% all_sps),]
mt <- mt[which(mt$spp %in% all_sps),]
ms <- ms[which(ms$spp %in% all_sps),]
pheno <- pheno[which(pheno$spp %in% all_sps),]
length(unique(gen_d$spp)) # 714
length(unique(gen_lf$spp)) # 877
length(unique(gen_fon$spp)) # 35219
length(unique(mt$spp)) # 162
length(unique(ms$spp)) # 275
length(unique(pheno$spp)) # 41511.1 Fix names at the genetic data
# Species to find
mycols <- c("reported_name_fixed","scientificName","kingdom","phylum","class","order","family","db_code")
sps_accepted_names <- data.frame(matrix(ncol = length(mycols), nrow = length(unique(all_sps))))
names(sps_accepted_names) <- mycols
sps_accepted_names$reported_name_fixed <- unique(all_sps)
tofind_ <- sps_accepted_names[which(is.na(sps_accepted_names$scientificName)),]
tofind_ <- unique(tofind_$reported_name_fixed)
tofind <- data.frame(matrix(nrow = length(tofind_), ncol = 8))
names(tofind) = c("scientificName", "kingdom", "phylum", "class", "order", "family", "db", "db_code")
tofind <- data.frame(species = tofind_, tofind)
tofind <- tofind %>%
mutate(across(everything(), as.character))
# retrieve sp names
sp_names_found <- Find_Sci_Names(sp_name = tofind$species)
# ----------------
# Summary
# ----------------
#
# N taxa:
# 36697
# N taxa found:
# |db | N|
# |:----|-----:|
# |GBIF | 35903|
# |ITIS | 76|
# |NCBI | 278|
# N taxa not found:
# 440
## Add found species names to the sps_accepted_names
all(sp_names_found$requested_name %in% sps_accepted_names$reported_name_fixed)
for(i in 1:length(sp_names_found$requested_name)){
tofill <- unique(which(sps_accepted_names$reported_name_fixed == sp_names_found$requested_name[i]))
sps_accepted_names$scientificName[tofill] <- sp_names_found$scientificName[i]
sps_accepted_names$kingdom[tofill] <- sp_names_found$kingdom[i]
sps_accepted_names$phylum[tofill] <-sp_names_found$phylum[i]
sps_accepted_names$class[tofill] <- sp_names_found$class[i]
sps_accepted_names$order[tofill] <- sp_names_found$order[i]
sps_accepted_names$family[tofill] <- sp_names_found$family[i]
sps_accepted_names$db[tofill] <- sp_names_found$db[i]
sps_accepted_names$db_code[tofill] <- sp_names_found$db_code[i]
sps_accepted_names$method[tofill] <- sp_names_found$method[i]
}
sps_accepted_names$spp = sps_accepted_names$reported_name_fixed
## Keep original taxa information for those species we could not find a name at GBIF
pos <- which(is.na(sps_accepted_names$scientificName))
sps_accepted_names$scientificName[pos] <- sps_accepted_names$spp[pos]
for(i in 1:nrow(mt)){
mt$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == mt$spp[i])]
}
for(i in 1:nrow(ms)){
ms$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == ms$spp[i])]
}
for(i in 1:nrow(gen_d)){
gen_d$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == gen_d$spp[i])]
}
for(i in 1:nrow(gen_lf)){
gen_lf$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == gen_lf$spp[i])]
}
for(i in 1:nrow(gen_fon)){
gen_fon$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == gen_fon$spp[i])]
}
for(i in 1:nrow(gen_lf)){
pheno$spp_new[i] <- sps_accepted_names$scientificName[which(sps_accepted_names$spp == pheno$spp[i])]
}
write.csv(mt,
here::here('Data/mtdna_harmo.csv'), row.names = FALSE)
write.csv(ms,
here::here('Data/msat_harmo.csv'), row.names = FALSE)
write.csv(gen_d,
here::here('Data/Deposited_data_genetic_diversity_dekort2021_harmo.csv'), row.names = FALSE)
write.csv(gen_lf,
here::here('Data/MacroPopGen_Database_final_areas_Lawrence_Fraser_2020_harmo.csv'), row.names = FALSE)
write.csv(gen_fon,
here::here('Data/Fonseca_etal_2023_EvoLetters_harmo.txt'), row.names = FALSE)
write.csv(pheno,
here::here('Data/PROCEEDv6_RatesDB_harmo.csv'), row.names = FALSE)12 Load harmonized genetic data
mt <- fread(here::here('Data/mtdna_harmo.csv'))
ms <- fread(here::here('Data/msat_harmo.csv'))
gen_d <- fread(here::here('Data/Deposited_data_genetic_diversity_dekort2021_harmo.csv'))
gen_lf <- fread(here::here('Data/MacroPopGen_Database_final_areas_Lawrence_Fraser_2020_harmo.csv'))
gen_fon <- fread(here::here('Data/Fonseca_etal_2023_EvoLetters_harmo.txt'))
pheno <- fread(here::here('Data/PROCEEDv6_RatesDB_harmo.csv'))12.1 Stats
# Gen
gen_sps <- unique(c(mt$spp_new, ms$spp_new, gen_d$spp_new, gen_lf$spp_new, gen_lf$spp_new, gen_fon$spp_new))
bsi_v1 <- intersect(biov1$spp, gen_sps)
# Break down
tot1 <- data.frame(N_species_with_gen_data = length(gen_sps),
v1_matches = length(bsi_v1))
tot1## N_species_with_gen_data v1_matches
## 1 36016 3145# N_species_with_gen_data v1_matches
# 36016 3145
# phenotypic
phen_sps <- unique(pheno$spp_new)
bsj_v1 <- intersect(biov1$spp, phen_sps)
# Break down
tot2 <- data.frame(N_species_with_pheno_data = length(phen_sps),
v1_matches = length(bsj_v1))
tot2## N_species_with_pheno_data v1_matches
## 1 411 269# N_species_with_pheno_data v1_matches
# 411 26912.2 Explore distribution
# Malin
ggplot(mt, aes(He)) +
geom_histogram() +
ggtitle("Malin's Mitochondrial")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 48 rows containing non-finite values (`stat_bin()`).
ggplot(ms, aes(He)) +
geom_histogram() +
ggtitle("Malin's Microsatellite")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# De Kort
ggplot(gen_d, aes(GDp)) +
geom_histogram() +
ggtitle("De Kort AFLP & Microsatellite")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Why De Kort data has the two peaks? It is due to the marker type
ggplot(gen_d, aes(GDp)) +
geom_histogram()+
facet_grid(.~Marker)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# But if we look at the normalized GDp, the problem is solved. Therefore, we should be using the normalized version of GDp from DeKort
ggplot(gen_d, aes(GDp_norm)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Lawrence & Fraser
ggplot(gen_lf, aes(as.numeric(He))) +
geom_histogram()+
ggtitle("Lawrence & Fraser Microsatellite")## Warning in FUN(X[[i]], ...): NAs introduced by coercion## Warning in FUN(X[[i]], ...): NAs introduced by coercion## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1282 rows containing non-finite values (`stat_bin()`).
# Fonseca et al.
ggplot(gen_fon, aes(as.numeric(Nucleotide_diversity))) +
geom_histogram()+
ggtitle("Fonseca et al. Mitochondrial DNA")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
13 Create a single genetic diversity dataset
# Malin
mt_sub <- mt %>%
mutate(long = as.numeric(lon),
lat = as.numeric(lat),
N = as.numeric(n), # Sample size
Marker = "Mitochondrial DNA") %>%
dplyr::filter(!is.na(He) | !He==0) %>%
dplyr::select(spp, He, long, lat, Marker)
mt_sub$Source = "Malin"
# mt_sub$Het_type = "He"
ms_sub <- ms %>%
mutate(long = as.numeric(lon),
lat = as.numeric(lat),
N = as.numeric(n), # Sample size
Marker = "Microsatellite") %>%
dplyr::filter(!is.na(He) | !He==0) %>%
dplyr::select(spp, He, long, lat, Marker)
ms_sub$Source = "Malin"
# ms_sub$Het_type = "He"
###################
# De Kort
# Jonathan R.:
# co-dominant refers to the fact that you can distinguish homozigous and heterozygous individuals, such as microsatellites
# dominant means that you either have the detection of the marker or not. but you don’t know if the individual is heterozygous or not, such as AFLP
# restriction enzymes are proteins that you use to cut the genomes into fragments. there are used for AFLP and allozymes, so not sure
# De Kort code for Marker:
# CD = Co-dominant
# D = Dominant
# ENZ = Enzime
gen_d_sub <- gen_d %>%
mutate(long = as.numeric(LONGITUDE),
lat = as.numeric(LATITUDE),
spp = spp_new,
He = as.numeric(GDp),
N = as.numeric(SampleSize), # Sample size
Marker = case_when(
Marker=="CD" ~ "Microsatellite",
Marker=="D" ~ "AFLP",
Marker=="ENZ" ~ "AFLP",
TRUE ~ as.character(Marker))) %>%
dplyr::filter(!is.na(He) | !He==0) %>%
dplyr::filter(!(Marker == "AFLP" & He > .5)) %>% # AFLPs > 0.5 are errors
dplyr::select(spp, He, long, lat, Marker)
gen_d_sub$Source = "De Kort"
# gen_d_sub$Het_type = "He"
# How DeKort calculated the normalized version of GDp
# gen_d_sub$He_harm <- NA
#
# m <- unique(gen_d_sub$Marker)
# for(i in 1:length(m)){
# pos <- which(gen_d_sub$Marker == m[i])
# gen_d_sub$He_harm[pos] <- scale(gen_d_sub$He[pos])
# }
# gen_d_sub$He_harm <- (gen_d_sub$He_harm - min(gen_d_sub$He_harm))/(max(gen_d_sub$He_harm)-min(gen_d_sub$He_harm))
###################
# Lawrence & Fraser
gen_lf$He <- as.numeric(gen_lf$He)## Warning: NAs introduced by coerciongen_lf$Ho <- as.numeric(gen_lf$Ho)## Warning: NAs introduced by coercionpos <- which(is.na(gen_lf$He))
gen_lf$He_new <- gen_lf$He
gen_lf$He_new[pos] <- gen_lf$Ho[pos]
# gen_lf$Het_type = "He"
# gen_lf$Het_type[pos] = "Ho"
gen_lf_sub <- gen_lf %>%
mutate(long = as.numeric(Long),
lat = as.numeric(Lat),
He = as.numeric(He_new),
spp = spp_new,
N = as.numeric(n), # Sample size
Marker = "Microsatellite") %>%
dplyr::filter(!is.na(He) | !He==0) %>%
dplyr::select(spp, He, long, lat, Marker,
# Het_type
) ## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `N = as.numeric(n)`.
## Caused by warning:
## ! NAs introduced by coerciongen_lf_sub$Source = "Lawrence & Fraser"
###################
# Fonseca et al.
gen_fon_sub <- gen_fon %>%
mutate(long = as.numeric(Longitude),
lat = as.numeric(Latitude),
He = Nucleotide_diversity) %>%
dplyr::filter(!is.na(Nucleotide_diversity) | !Nucleotide_diversity==0) %>%
dplyr::select(spp, He, long, lat)
gen_fon_sub$Source = "Fonseca"
gen_fon_sub$Marker = "Mitochondrial DNA"
###################
# Group all
gen_div <- rbind(mt_sub %>% dplyr::select(names(mt_sub)),
ms_sub %>% dplyr::select(names(mt_sub)),
gen_d_sub %>% dplyr::select(names(mt_sub)),
gen_lf_sub %>% dplyr::select(names(mt_sub)),
gen_fon_sub %>% dplyr::select(names(mt_sub)))
gen_div <- na.omit(gen_div)
gen_div$spp <- as.factor(gen_div$spp)
gen_div$Marker <- as.factor(gen_div$Marker)
gen_div$Source <- as.factor(gen_div$Source)14 Harmonize genetic data
- He_harm: transformation used by DeKort et al 2021 (Nature Communications). Makes markers range from 0-1.
- scale values at each marker type
- standardize (x - min)/(max-min)
- He_harm2: transformation suggested by Malin. Applies the function transform01 before a logit transformation.
- transform01 />/> Moves He values slightly inward so that a logit transformation is possible.
- apply logit transformation
- He_harm3: He_harm2 centered in zero.
gen_div$He_harm <- NA
gen_div$He_harm2 <- NA
gen_div$He_harm3 <- NA
m <- unique(gen_div$Marker)
for(i in 1:length(m)){
# Select marker type
pos <- which(gen_div$Marker == m[i])
# Apply DeKort transformation
gen_div$He_harm[pos] <- deKort_trans(gen_div$He[pos])
# Apply logit transformation
gen_div$He_harm2[pos] <- logit_trans(gen_div$He[pos])
# Centered in zero
gen_div$He_harm3[pos] <- scale(gen_div$He_harm2[pos])
}
# Filter the species in Bioshifts
gen_div <- gen_div %>% dplyr::filter(spp %in% unique(biov1$spp))
# remove outliers
gen_metrics <- c("He_harm","He_harm2","He_harm3")
for (i in 1:length(gen_metrics)){
my_gem <- as.numeric(data.frame(gen_div %>% select(gen_metrics[i]))[,1])
quartiles <- quantile(my_gem, probs=c(.05, .95), na.rm = FALSE)
IQR <- IQR(my_gem)
Lower <- quartiles[1] - 1.5*IQR
Upper <- quartiles[2] + 1.5*IQR
gen_div <- subset(gen_div, my_gem > Lower & my_gem < Upper)
}14.1 Avg by location
We averaged genetic diversity by location (same species, in the same XY coordinates)
# How many species with >1 gen div measurements at the same site?
# Check how many obs per site and marker type exist
N_obs <- gen_div %>%
group_by(spp, long, lat) %>%
tally() %>%
dplyr::filter(n>1)
DT::datatable(N_obs)## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html# Is there any species with >1 marker type in the same location?
gen_div %>%
group_by(spp, long, lat, Marker) %>%
dplyr::summarise(N = length(unique(spp))) %>%
dplyr::filter(N>1)## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.## # A tibble: 0 × 5
## # Groups: spp, long, lat [0]
## # ℹ 5 variables: spp <fct>, long <dbl>, lat <dbl>, Marker <fct>, N <int># No, there are not
# Is there any species with >1 Source type in the same location?
gen_div %>%
group_by(spp, long, lat, Source) %>%
dplyr::summarise(N = length(unique(spp))) %>%
dplyr::filter(N>1)## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.## # A tibble: 0 × 5
## # Groups: spp, long, lat [0]
## # ℹ 5 variables: spp <fct>, long <dbl>, lat <dbl>, Source <fct>, N <int># No, there are not
# Avg by site and species
gen_div <- gen_div %>%
group_by(spp, long, lat) %>%
dplyr::summarise(He = median(He),
He_harm = median(He_harm),
He_harm2 = median(He_harm2),
He_harm3 = median(He_harm3),
Source = paste(Source, sep = ", "),
Marker = paste(Marker, sep = ", "))## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `summarise()` has grouped output by 'spp', 'long', 'lat'. You can override
## using the `.groups` argument.unique(gen_div$Source)## [1] "Fonseca" "De Kort" "Lawrence & Fraser"
## [4] "Malin"unique(gen_div$Marker)## [1] "Mitochondrial DNA" "AFLP" "Microsatellite"14.2 Which species?
gen_div <- merge(gen_div,
splist[,-7],
by.x="spp",
by.y="scientificName",
all.x = T)
toplot_ <- gen_div %>%
group_by(Group) %>%
dplyr::summarise(Obs = length(spp),
Species = length(unique(spp))) %>%
gather(var, N, -c(Group))
ggplot(toplot_, aes(x = Group, y = N))+
ggtitle("N species by Group")+
geom_bar(stat="identity")+
geom_text(aes(y=N, label=N,
hjust = ifelse(N<max(N),-.1,1.1)), vjust=0.2, size=3,
position = position_dodge(0.9))+
theme_classic()+
coord_flip()+
facet_wrap(.~var, scales = "free", ncol=1)
14.3 Distribution of He
# correlations
ggpairs(gen_div,
columns = c("He","He_harm","He_harm2","He_harm3"))
ggpairs(gen_div,
columns = c("He","He_harm","He_harm2","He_harm3"),
aes(color = Marker,
alpha = 0.5)) 
ggpairs(gen_div,
columns = c("He","He_harm","He_harm2","He_harm3"),
aes(color = Source,
alpha = 0.5))
to_plot <- gen_div %>%
select(Group,He_harm,He_harm2,He_harm3) %>%
filter(Group %in% c("Mammal","Fish","Plant", "Bird")) %>%
gather(metric, value, -Group)
ggplot(to_plot, aes(value, fill = Group, color = Group))+
geom_density(alpha = .3)+
scale_color_viridis_d()+
scale_fill_viridis_d()+
facet_wrap(.~metric, scales = "free")
14.4 High He with latitude?
14.4.1 Absolution latitude
toplot <- gen_div %>%
mutate(Hemisphere = ifelse(lat<0, "South", "North"),
lat_abs = abs(lat),
He_harm = scale(He_harm),
He_harm3 = scale(He_harm3))
ggplot(toplot, aes(x = lat_abs, y = He_harm, color = Group))+
geom_point(alpha = .1)+
stat_smooth(method = "lm")+
facet_wrap(.~Group,scales = "free")## `geom_smooth()` using formula = 'y ~ x'
mod1 <- lm(He_harm~lat*Group, toplot)
emtrends(mod1, ~ lat*Group, var = "lat")## lat Group lat.trend SE df lower.CL
## 46.4 Amphibian 0.004164 0.002302 11085 -0.000348
## 46.4 Ascidians tunicates and sea squirts nonEst NA NA NA
## 46.4 Bird 0.004774 0.000767 11085 0.003271
## 46.4 Fish 0.012919 0.000580 11085 0.011783
## 46.4 Insect 0.000214 0.001010 11085 -0.001765
## 46.4 Mammal -0.001732 0.002550 11085 -0.006731
## 46.4 Molluscs 0.040404 0.145179 11085 -0.244172
## 46.4 Plant -0.010242 0.000778 11085 -0.011768
## 46.4 Reptile -0.011657 0.015071 11085 -0.041199
## 46.4 Spider -0.000195 0.008215 11085 -0.016299
## upper.CL
## 0.00868
## NA
## 0.00628
## 0.01406
## 0.00219
## 0.00327
## 0.32498
## -0.00872
## 0.01789
## 0.01591
##
## Confidence level used: 0.9514.5 Spatial pattern
mundi <- terra::vect(rnaturalearth::ne_coastline(scale = 110, returnclass = "sp"))## Warning: The `returnclass` argument of `ne_download()` sp as of rnaturalearth 1.0.0.
## ℹ Please use `sf` objects with {rnaturalearth}, support for Spatial objects
## (sp) will be removed in a future release of the package.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# get vect gen div
vect_div <- terra::vect(gen_div,geom=c("long","lat"),crs=crs(mundi))
# get vect centroids bioshifts
vect_biov1 <- terra::vect(biov1 %>%
filter(spp %in% gen_div$spp),
geom=c("centroid.x","centroid.y"),crs=crs(mundi))
# get raster bioshifts shp files study areas
get_raster_bioshifts = "NO"
if(get_raster_bioshifts=="YES"){
my_ext = terra::ext(mundi)
my_crs = crs(mundi)
rast_biov1 <- terra::rast(my_ext, crs = my_crs, res = 0.5)
values(rast_biov1) <- 0
fgdb <- "C:/Users/brunn/ShadowDrive/CreateGeodatabaseBioShifts/Data/ShapefilesBioShiftsv3"
fc_list <- list.files(fgdb,pattern = ".shp")
fc_list <- gsub(".shp","",fc_list)
fc_list <- fc_list[which(fc_list %in% unique(as.data.frame(vect_biov1)$ID))]
for(i in 1:length(fc_list)){ cat("\r",i,"from",length(fc_list))
tmp = terra::vect(here::here(fgdb, paste0(fc_list[i],".shp")))
tmp = terra::cells(rast_biov1,tmp)
tmp_cell = tmp[,2]
tmp_vals = rast_biov1[tmp_cell][,1]
rast_biov1[tmp_cell] = tmp_vals+1
}
names(rast_biov1) <- "SA"
rast_biov1[rast_biov1==0] <- NA
writeRaster(rast_biov1, here::here("Data/raster_bioshifts_SA.tif"), overwrite = TRUE)
} else {
rast_biov1 <- terra::rast(here::here("Data/raster_bioshifts_SA.tif"))
}
values_biov1 <- na.omit(values(rast_biov1))
ggplot()+
ggtitle("Genetic diversity data")+
geom_spatvector(data=mundi)+
geom_spatvector(data=vect_div,aes(color = Marker))+
theme_blank()
ggplot()+
ggtitle("Genetic diversity data")+
geom_spatvector(data=mundi)+
geom_spatvector(data=vect_div,aes(color = Source))+
theme_blank()
ggplot()+
ggtitle("Bioshifts data \n(Centroid of study areas)")+
geom_spatvector(data=mundi)+
geom_spatvector(data=vect_biov1, aes(color = ECO))+
theme_blank()
ggplot()+
ggtitle("Bioshifts data \n(Overlap study areas)")+
geom_spatraster(data=rast_biov1)+
scale_fill_whitebox_b(
palette = "muted",
na.value = "white",
breaks = seq(min(values_biov1), max(values_biov1), 1))+
geom_spatvector(data=vect_biov1, aes(color = ECO))+
geom_spatvector(data=mundi)+
theme_blank()
15 Phenotypic rates
pheno_rate <- pheno %>%
mutate(long = as.numeric(sample1.longitude),
lat = as.numeric(sample1.latitude),
trait1 = as.numeric(mean1),
trait2 = as.numeric(mean2),
spp = spp_new,
Trait = trait_type,
Years = as.numeric(years),
Gen = as.numeric(generations)) %>%
dplyr::select(spp, long, lat, Trait, trait1, trait2, Years, Gen) %>%
dplyr::filter(!is.na(trait1) & !is.na(trait2))
pheno_rate$Source = "PROCEED"15.1 Calculate rate & harmozine
for(i in 1:nrow(pheno_rate)){
sppi <- pheno_rate$spp[i]
traiti <- pheno_rate$Trait[i]
pos_spp_trait_i <- which(pheno_rate$spp == sppi & pheno_rate$Trait == traiti)
pheno_rate$PR1[i] <- (pheno_rate$trait2[i] - pheno_rate$trait1[i]) / mean(c(pheno_rate$trait2[pos_spp_trait_i], pheno_rate$trait1[pos_spp_trait_i])) * pheno_rate$Years[i]
pheno_rate$PR2[i] <- (pheno_rate$trait2[i] - pheno_rate$trait1[i]) / sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i], pheno_rate$trait1[pos_spp_trait_i])))^2 * pheno_rate$Years[i]
}## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs produced
## Warning in sqrt(mean(c(pheno_rate$trait2[pos_spp_trait_i],
## pheno_rate$trait1[pos_spp_trait_i]))): NaNs producedpheno_rate$PR_harm <- NA
m <- unique(pheno_rate$Trait)
for(i in 1:length(m)){
pos <- which(pheno_rate$Trait == m[i])
# DeKort
p <- scale(pheno_rate$PR1[pos])
p <- (p - min(p, na.rm = T))/(max(p, na.rm = T)-min(p, na.rm = T))
pheno_rate$PR_harm[pos] <- p
}
# gen_div %>%
# group_by(spp) %>%
# tally() %>%
# summary()15.2 Distribution of pheno rates
# Look into phenotic rates
ggplot(pheno_rate, aes(PR1)) +
geom_histogram() +
facet_wrap(Trait~., scales = "free") +
ggtitle("Raw data")## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
ggplot(pheno_rate, aes(PR_harm)) +
geom_histogram() +
facet_wrap(Trait~., scales = "free") +
ggtitle("Harmonization following De Kort")## Warning: Removed 1 rows containing non-finite values (`stat_bin()`).
16 Merge genetic and bioshifts data
We tried several methods for merging genetic data and bioshifts.
Method 1: Normal merge Uses the merge function to aggregate bioshifts and gen div data. This creates multiple false duplicates.
Method 2: Median He per species. This ignores spatial proximity of He values to range shift values.
Method 3: Median He per Param & species Use lower latitude TE and higher latitude as LE (North Hemisphere). Although this collects He values based on latitude, the proximity of He to the study areas where range shifts were observed is ignored.
Method 4: Distance based approach. Calculates distance weighted mean of He to the centroid of the study area.
Method 5: Location based approach. Get the closest He to the centroid (for centroid shift), to the max latitude (for LE shift, at the North hemisphere), and to the min latitude (for TE shift, at the North hemisphere) of the study area.
We decided that Method 5 is the best one.
17 Method 1 - Normal merge
This is not the correct way to merge because it creates multiple false duplicates.
18 Method 2 - Median He per species
This ignores spatial proximity of He values to range shift values.
gen_div_avg <- gen_div %>%
group_by(spp) %>%
dplyr::summarise(N = length(spp),
He_harm = median(He_harm),
He_harm2 = median(He_harm2),
He_harm3 = median(He_harm3))
#merge all data frames in list
gen_data_v1_avg <- append(list(gen_div_avg), list(biov1)) %>% reduce(full_join, by='spp')
gen_data_v1_avg <- gen_data_v1_avg %>% dplyr::filter(!is.na(He_harm), !is.na(SHIFT))
# N species
length(unique(gen_data_v1_avg$spp))## [1] 318618.1 Save data
write.csv(gen_data_v1_avg,here::here("Data/gen_data_final_m2.csv"),row.names = FALSE)19 Method 3 - Median He per Param & species
Use lower latitude TE and higher latitude as LE (North Hemisphere)./ Although this collects He values based on latitude, the proximity of He to the study areas where range shifts were observed is ignored.
20 Method 4 - Based on distance
Calculate distance weighted mean He for each species in the bioshifts database./ This method collects He values more close to the centroid of the study area.
21 Method 5 - Based on location
Get the closest He to the centroid (for O), to the max latitude (for LE, at the North hemisphere), and to the min latitude (for TE, at the North hemisphere) of the study area.
# sp list
m <- unique(biov1$spp[which(biov1$spp %in% gen_div$spp)])
gen_data_v1_dist2 <- data.frame()
# sp2go="Alces alces"
# sp2go="Sylvia atricapilla"
for(i in 1:length(m)){ cat(i, "from", length(m), "\r")
sp2go <- m[i]
# subset genetic data
sub_gen <- subset(gen_div[,1:9], spp == sp2go)
sub_gen_v <- terra::vect(sub_gen,
geom=c("long", "lat"),
crs = "+proj=longlat +datum=WGS84 +no_defs")
# subset bioshifts data
sub_bio <- subset(biov1, spp == sp2go)
# study areas for species i
areas <- unique(sub_bio$ID)
gen_data_v1_dist2_tmp <- data.frame()
# get genetic data for species i in each study area
for(j in 1:nrow(sub_bio)){
# bioshifts in study area j and range pos r
sub_bio_area_j_tmp <- sub_bio[j,]
range_pos2go <- sub_bio_area_j_tmp$Param
coord_names <- NA
coord_names <- if(range_pos2go == "O"){ c("centroid.x", "centroid.y") } else {coord_names}
coord_names <- if(range_pos2go == "LE" & sub_bio_area_j_tmp$centroid.y > 0){ c("max_lat.x", "max_lat.y") } else {coord_names}
coord_names <- if(range_pos2go == "LE" & sub_bio_area_j_tmp$centroid.y < 0){ c("min_lat.x", "min_lat.y") } else {coord_names}
coord_names <- if(range_pos2go == "TE" & sub_bio_area_j_tmp$centroid.y < 0){ c("max_lat.x", "max_lat.y") } else {coord_names}
coord_names <- if(range_pos2go == "TE" & sub_bio_area_j_tmp$centroid.y > 0){ c("min_lat.x", "min_lat.y") } else {coord_names}
sub_bio_area_j_v = data.frame(sub_bio_area_j_tmp)[,coord_names]
names(sub_bio_area_j_v) <- c("long", "lat")
sub_bio_area_j_v <- terra::vect(unique(sub_bio_area_j_v[,c("long", "lat")]),
geom=c("long", "lat"),
crs = "+proj=longlat +datum=WGS84 +no_defs")
# plot(terra::vect(rbind(sub_gen[,c("long", "lat")], sub_bio_area_j_tmp[,c("long", "lat")]),
# geom=c("long", "lat"),
# crs = "+proj=longlat +datum=WGS84 +no_defs"))
# plot(sub_bio_area_j_v, col = "red", add=T)
dists <- terra::distance(sub_bio_area_j_v, sub_gen_v)[1,]
dists_order <- order(dists)[1]
min_dist <- dists[dists_order] # distance to closest
# the closest
sub_gen_tmp <- sub_gen[dists_order,]
sub_gen_tmp$min_dist <- min_dist
# weighted distance
sub_gen_tmp$He_harm_w <- weighted.mean(sub_gen$He_harm, 1/(dists^2)) # inverse of the distance
sub_gen_tmp$He_harm2_w <- weighted.mean(sub_gen$He_harm2, 1/(dists^2)) # inverse of the distance
sub_gen_tmp$He_harm3_w <- weighted.mean(sub_gen$He_harm3, 1/(dists^2)) # inverse of the distance
gen_data_v1_dist2_tmp <- rbind(gen_data_v1_dist2_tmp,
append(list(sub_gen_tmp),
list(sub_bio_area_j_tmp)) %>%
reduce(full_join, by="spp"))
}
gen_data_v1_dist2 <- rbind(gen_data_v1_dist2,
gen_data_v1_dist2_tmp)
}
gen_data_v1_dist2 <- gen_data_v1_dist2 %>% dplyr::filter(!is.na(He), !is.na(SHIFT))21.1 Save data
write.csv(gen_data_v1_dist2,here::here("Data/gen_data_final_m5.csv"),row.names = FALSE)22 Explore data
# n range shifts
nrow(gen_data_v1_dist2)## [1] 11849# n genetic diversity measurements (after averaging by location)
dim(gen_div)## [1] 11104 1722.1 The closest He vs distance weighted He
plot(gen_data_v1_dist2$He_harm,gen_data_v1_dist2$He_harm_w)
plot(gen_data_v1_dist2$He_harm2,gen_data_v1_dist2$He_harm2_w)
plot(gen_data_v1_dist2$He_harm3,gen_data_v1_dist2$He_harm3_w)
22.2 Which species?
# N species
length(unique(gen_data_v1_dist2$spp))## [1] 3186# Use only plus plus or minus minus
table(gen_data_v1_dist2$shift_vel_sign)##
## negneg negpos posneg pospos
## 203 4393 297 6956# N species
length(unique(gen_data_v1_dist2$spp))## [1] 3186toplot_ <- gen_data_v1_dist2 %>%
group_by(Group) %>%
dplyr::summarise(Species = length(unique(spp)),
Obs = length(spp))
toplot_ <- reshape2::melt(toplot_)## Using Group as id variablesggplot(toplot_, aes(x = Group, y = value))+
geom_bar(stat="identity")+
geom_text(aes(y=value, label=value),
hjust = -.1, vjust=0.2, size=3,
position = position_dodge(0.9))+
theme_classic()+
coord_flip()+
facet_wrap(.~variable, scales = "free", ncol = 1)
22.3 Compare lag values of the full bioshifts vs the subset (genetic) dataset
toplot <- rbind(
data.frame(dataset = "Full",
lags = biov1$lag2,
shifts = biov1$SHIFT),
data.frame(dataset = "Subset",
lags = gen_data_v1_dist2$lag2,
shifts = gen_data_v1_dist2$SHIFT))
# lags
ggplot(toplot, aes(x = dataset, y=lags, fill = dataset, color = dataset))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(toplot$lags, c(0.1, 0.9), na.rm = T))## Warning: Removed 8175 rows containing non-finite values (`stat_boxplot()`).
tapply(toplot$lags, toplot$dataset, summary)## $Full
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -145.1653 -0.4280 0.5907 0.3604 2.1868 13.1259
##
## $Subset
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -145.1653 -0.8960 0.5397 0.1183 2.1601 13.1259mod1 <- aov(lags~dataset, data = toplot)
summary(mod1)## Df Sum Sq Mean Sq F value Pr(>F)
## dataset 1 499 498.9 21.84 2.98e-06 ***
## Residuals 42086 961660 22.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# shift
ggplot(toplot, aes(x = dataset, y=shifts, fill = dataset, color = dataset))+
geom_boxplot(alpha = .5, outlier.shape = NA)+
scale_y_continuous(limits = quantile(toplot$shifts, c(0.1, 0.9), na.rm = T))## Warning: Removed 8416 rows containing non-finite values (`stat_boxplot()`).
tapply(toplot$shifts, toplot$dataset, summary)## $Full
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -119.1696 -0.4362 0.2917 1.1682 2.4222 146.3000
##
## $Subset
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -119.1696 -0.3014 0.3705 1.3252 2.6621 146.3000mod1 <- aov(shifts~dataset, data = toplot)
summary(mod1)## Df Sum Sq Mean Sq F value Pr(>F)
## dataset 1 210 209.92 6.866 0.00879 **
## Residuals 42086 1286656 30.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There are difference in mean values of lags, but no difference in mean shift values, between the full and subset datasets.
22.4 Lag
22.4.1 General pattern
22.4.1.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3, aes(color = SHIFT_abs))+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.1.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.1.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.2 By Param
22.4.2.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.2.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.2.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.3 By Group
22.4.3.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.3.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.3.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.4 By Group /* Param
22.4.4.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.4.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.4.4.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5 Lag 2
22.5.1 General pattern
22.5.1.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.1.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.1.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.2 By Param
22.5.2.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.2.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.2.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.3 By Group
22.5.3.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.3.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.3.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.4 By Group /* Param
22.5.4.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.4.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.5.4.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = lagC2, color = SHIFT_abs))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("lagC (Velocity - Shift)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6 SHIFT
22.6.1 General pattern
22.6.1.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.1.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.1.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.2 By Param
22.6.2.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.2.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = log1p(sqrt(abs(SHIFT_cor))), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = log1p(sqrt(abs(SHIFT_cor))), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.2.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Param, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.3 By Group
22.6.3.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.3.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.3.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(.~Group, scales = "free", ncol = 3)## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.4 By Group /* Param
22.6.4.1 He_harm
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm, y = (abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm, y = (abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.4.2 He_harm2
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm2, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_wrap(Param~Group, scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
22.6.4.3 He_harm3
gen_data_v1_dist2 %>%
dplyr::filter(Type == "LAT") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Latitude")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 146 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 146 rows containing missing values (`geom_point()`).
gen_data_v1_dist2 %>%
dplyr::filter(Type == "ELE") %>%
ggplot(aes(x = He_harm3, y = sqrt(abs(SHIFT_cor)), color = lag))+
ggtitle("Elevation")+
xlab("Genetic diversity (He harm logit)")+
ylab("Range shift (km/year)") +
scale_color_viridis_c(direction = -1)+
geom_point(alpha = .5, size = 3)+
theme_bw()+
geom_smooth(method = "lm", color = "black", size=1)+
stat_smooth(geom="line", method = "lm", alpha=0.3, se = FALSE,
aes(by=as.factor(Article_ID)), color = "black")+
facet_grid(Group~Param,scales = "free")## Warning in stat_smooth(geom = "line", method = "lm", alpha = 0.3, se = FALSE, :
## Ignoring unknown aesthetics: by## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 15 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).